NSF Postdoctoral Fellowship in Biology FY 2020: Predicting Evolution Through Quantitative Dissection of the Bacterial Proteome

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This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2020, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. This research will use modern technologies to predict how bacteria will evolve in response to environmental pressures, an ability necessary to understand the evolution of dangerous traits such as antibiotic resistance. While an organism’s genetic sequence records its life history, it is difficult to predict how the organism will evolve based on sequence alone. This is due to the inability to predict how mutations can affect the organism’s ability to reproduce, termed its fitness. The project will use recently discovered biological laws to explore how mutations alter the cell’s energy budget to make new proteins. This information will be used to produce a mathematical model to understand how mutations are tied to fitness. The work will develop an experimental system where bacterial evolution can be controlled and predicted, which may have real world applications in medicine and agriculture. This project will augment the fellow’s diverse background through the application of new technology. To broaden the impact of the work, the fellow will train undergraduate and graduate students in the sponsoring scientists’ labs.Recently developed bacterial “growth laws” predict a strong correlation between the cellular growth rate and the proteomic fraction dedicated to ribosomes. This implies that adaptive evolution should skew the allocation of cellular resources towards maintaining ribosomes rather than unnecessary proteins. The objective of this work is to dissect how adaptive mutations modulate the allocation of resources to maximize growth rate, culminating in a mathematical model to predict evolution. The fellow will rely on novel sequencing-based experiments that permit time-resolved measurement of the emergence of novel beneficial mutations in large microbial populations. The effect of these beneficial mutations on the proteomic composition will be monitored via mass spectrometry and RNA-sequencing, illuminating how beneficial mutations alter the allocation of resources. Given a pairwise mapping of the identity of a beneficial mutation and the gene expression profile, the fellow aims to quantitatively map the fitness landscape. It then becomes possible to predict how changes in gene expression lead to changes in fitness. The fellow’s background in modeling and experimentation will be strengthened by the development and application of sequencing-based experiments. The broader impacts will include the training of undergraduate and graduate students in the design, execution, and interpretation of project experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.